The optimal trade-off between interest rate risk and annual return of bond ladders

Bond laddering is a popular fixed-income investment strategy. The main purpose of this paper is to develop a methodology for determining private investors’ most interest rate risk (IRR)-return-efficient investment horizon for bond ladders (BLs), which are virtually free of credit risk. Two IRR measures of a continuously rolling and homogenous BL (CRHBL) are analytically derived under the assumption that interest rates are martingales. The first measure is the modified duration, which assumes a flat term structure of interest rates. However, this assumption is not fully supported by the empirical data and, thus, an additional IRR measure is proposed. Under each of these two measures, the ratios between the annual return in excess of the demand deposit rate and IRR of CRHBLs with different investment horizons are calculated. As expected, CRHBLs with rather low IRR are most risk-return-efficient. The results for the theoretical CRHBLs also apply to “real-world” discrete BLs. Thus, the proposed methodology can help private investors construct IRR-return-efficient discrete BLs.     

Tail mutual exclusivity and Tail-VaR lower bounds

In this paper, we extend the concept of mutual exclusivity proposed by [Dhaene, J. & Denuit, M. (1999). The safest dependence structure among risks. Insurance: Mathematics and Economics 25, 11–21] to its tail counterpart and baptize this new dependency structure as tail mutual exclusivity. Probability levels are first specified for each component of the random vector. Under this dependency structure, at most one exceedance over the corresponding Value-at-Risks (VaRs) is possible, the other components being zero in such a case. No condition is imposed when all components stay below the VaRs. Several properties of this new negative dependence concept are derived. We show that this dependence structure gives rise to the smallest value of Tail-VaR (TVaR) of a sum of risks within a given Fréchet space, provided that the probability level of the TVaR is close enough to one.     

Social strategies that work

Although most companies have collected lots of friends and followers on social platforms such as Facebook, few have succeeded in generating profits there. That's because they merely port their digital strategies into social environments by broadcasting their commercial messages or seeking customer feedback. To succeed on social platforms, says Harvard Business School's Piskorski, businesses need to devise social strategies that are consistent with users' expectations and behavior in these venues--namely, people want to connect with other people, not with companies. The author defines successful social strategies as those that reduce costs or increase customers' willingness to pay by helping people establish or strengthen relationships through doing free work on a company's behalf. Citing successes at Zynga, eBay, American Express, and Yelp, Piskorski shows that social strategies can generate profits by helping people connect in exchange for tasks that benefit the company such as customer acquisition, marketing, and content creation. He lays out a systematic way to build a social strategy and shows how a major credit card company he advised used the method to roll out its own strategy.     

Technical Analysis as a Rational Tool of Decision Making for Professional Traders

The psychological background of technical analysis usage is investigated to further explain the popularity and common usage of technical analysis as an investment decision tool. Attitudes toward technical analysis of professional futures market traders and neophyte investors, represented by finance students, were examined. Technical analysis is one of the most popular methods supporting investment decisions and it is much more popular among future market traders than among neophyte investors. The concept of processing information was used to explain this phenomenon. Neophyte investors are more experiential and intuition-driven while using technical analysis models, while futures market traders are more rationally driven. Technical analysis methods help professional traders on futures markets, which are less transparent than regulated stock markets, to process information; those methods are perceived by them as rational, cognitive tools supporting their decision making.     

To buy or not to buy? The value of contradictory analyst signals

We study the predictive ability of individual analyst target price changes for post-event abnormal stock returns within each recommendation category. Although prior studies generally demonstrate the investment value of target prices, we find that target price changes do not cause abnormal returns within each recommendation level. Instead, contradictory analyst signals (e.g., strong buy reiterations with large target price decreases) neutralize each other, whereas confirmatory signals reinforce each other. Further, our analysis reveals that large target price downgrades can be explained by preceding stock price decreases. However, upgrades are not preceded by stock price increases, thereby demonstrating asymmetric analyst behavior when adjusting target prices to stock prices. Our results suggest that investors should treat recommendations with caution when they are issued with large contradictory target price changes. Thus, instead of blindly following a recommendation, investors might put more weight on the change in the corresponding target price and consider transaction costs.     

On measuring synchronization of bulls and bears: The case of East Asia

This paper implements estimation and testing procedures for comovements of stock market “cycles” or “phases” in Asia. We extend the Harding and Pagan [Harding, D., Pagan, A.P., 2006. Synchronization of cycles. Journal of Econometrics 132 (1), 59–79] test for strong multivariate nonsynchronization (SMNS) between business cycles to a test that allows for an imperfect degree of multivariate synchronization between stock market cycles. Moreover, we propose a test for endogenously determining structural change in the bivariate and multivariate synchronization indices. Upon applying the technique to five Asian stock markets we find a significant increase in the cross country comovements of Asian bullish and bearish periods in 1997. A power study of the stability test suggests that the detected increase in comovement is more of a sudden nature (i.e. contagion or “Asian Flu”) instead of gradual (i.e. financial integration). It is furthermore argued that stock market cycles and their propensity toward (increased) synchronization contain useful information for both investors, policy makers and financial regulators.     

Credit cycles and macro fundamentals

We use an intensity-based framework to study the relation between macroeconomic fundamentals and cycles in defaults and rating activity. Using Standard and Poor's U.S. corporate rating transition and default data over the period 1980–2005, we directly estimate the default and rating cycle from micro data. We relate this cycle to the business cycle, bank lending conditions, and financial market variables. In line with earlier studies, the macro variables appear to explain part of the default cycle. However, we strongly reject the correct dynamic specification of these models. The problem is solved by adding an unobserved dynamic component to the model, which can be interpreted as an omitted systematic credit risk factor. By accounting for this latent factor, many of the observed macro variables loose their significance. There are a few exceptions, but the economic impact of the observed macro variables for credit risk remains low. We also show that systematic credit risk factors differ over transition types, with risk factors for downgrades being noticeably different from those for upgrades. We conclude that portfolio credit risk models based only on observable systematic risk factors omit one of the strongest determinants of credit risk at the portfolio level. This has obvious consequences for current modeling and risk management practices.     

Mean–variance and mean–semivariance portfolio selection: a multivariate nonparametric approach

While univariate nonparametric estimation methods have been developed for estimating returns in mean-downside risk portfolio optimization, the problem of handling possible cross-correlations in a vector of asset returns has not been addressed in portfolio selection. We present a novel multivariate nonparametric portfolio optimization procedure using kernel-based estimators of the conditional mean and the conditional median. The method accounts for the covariance structure information from the full set of returns. We also provide two computational algorithms to implement the estimators. Via the analysis of 24 French stock market returns, we evaluate the in-sample and out-of-sample performance of both portfolio selection algorithms against optimal portfolios selected by classical and univariate nonparametric methods for three highly different time periods and different levels of expected return. By allowing for cross-correlations among returns, our results suggest that the proposed multivariate nonparametric method is a useful extension of standard univariate nonparametric portfolio selection approaches.     

Workforce Composition and Individual Wages — An Employer–Employee Data Analysis

The study analyses the impact of workforce composition and employee isolation — based on age, gender and citizenship — on entry wages of new employment relationships in German firms using employer–employee data. We allow for heterogeneous effects across distinct groups of workers and include worker and firm fixed effects to account for selection effects and unobserved heterogeneity. The results point to a negative impact of gender and age diversity for males and females, natives and foreigners and workers across the skill spectrum. Only for high-skilled workers, the negative effect of gender diversity is not statistically significant. Females receive, in addition, relatively low entry wages in establishments with a rather old workforce. With regard to the relative position, mainly gender isolation tends to exert an important influence on entry wages. The effect is positive only for females. In contrast, we estimate significant negative wage effects for males, natives, medium- and high-skilled workers. An international background of a firm's workforce and cultural isolation do not appear to generally affect entry wages.